Large Language Models Miss the Multi-Agent Mark

Why Current MAS LLMs Research Falls Short of Classical Multi-Agent Systems Principles

Published

May 27, 2025

Authors: E. La Malfa et al.

Published on Arxiv: 2025-05-27

Link: http://arxiv.org/abs/2505.21298v1

Institutions: Department of Computer Science, University of Oxford • Department of Informatics, King’s College London • Department of Engineering, University of Oxford • University of Sussex

Keywords: Multi-Agent Systems, Large Language Models, Emergent Behavior, Agent Communication Languages, Natural Language Communication, Asynchronous Systems, Theory of Mind, Social Intelligence, Reinforcement Learning, Environment Design, Benchmarks, Communication Protocols

Recent research efforts have focused on Multi-Agent Systems composed of Large Language Models (MAS LLMs), aiming to solve complex problems by coordinating several LLMs. However, the implementations often draw on terminology from classical MAS without faithfully reproducing fundamental MAS principles like social agency, advanced environment design, or rigorous measures of emergent behavior.

To address these observed discrepancies and advance the state of MAS LLMs, this position paper offers several key contributions:

Following this detailed analysis, the authors highlight critical weaknesses in existing MAS LLM frameworks:

In conclusion, the paper synthesizes its critique and recommendations as follows: